Advancing precision oncology with AI-powered genomic analysis

Abstract

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

Keywords: artificial intelligence; bioinformatics; genomics; precision oncology; therapeutics and analysis resource.

FIGURE 1

The pre-processing, variants identification, classifications, and comparison to known variants during the raw variant step and sorting of identified variants on specific criteria. Next, the accuracy of the data is enhanced before the full annotation and evaluation of variants.

FIGURE 2

Whole Genome Sequencing (WGS) from Patient to Clinical Report-WGS could provide valuable clinical insights—either by confirming a diagnosis or suggesting alternative treatment options. After patient consent, a sample of whole blood or tumor tissue is sent to a specialized laboratory equipped for WGS. The skilled professionals meticulously analyze the sequence data. A multidisciplinary team (MDT) establish a definitive diagnosis and evaluate the clinical significance of the detected variants. Once finalized, the clinical report is reviewed by attending physician and the results are discussed with the patient, which includes the implications of the findings, their impact on the patient’s condition, and recommended next steps. If the initial analysis does not identify a disease-causing variant, the stored WGS data is periodically re-analyzed (inner grey arrow). The continuous process allows for the incorporation of new scientific discoveries, potentially leading to a diagnosis without requiring further hospitalization or additional sampling. Additionally, other clinically relevant insights, such as pharmacogenetic data can be extracted from the WGS data to enhance patient care. This figure is adapted from Bagger, FO. et al. BMC Med Genomics 17, 39 (2024).